A database may be created and maintained by a wide variety of users in an organization (e.g., Target®, eBay®, Walmart®, etc.). Over time, different engineers may use their own semantics when defining elements, variables, and/or attributes of the database. This may create semantic complexity that may make it difficult for others to run queries against the database without extensive experimentation.
For example, an analyst may seek to perform queries against the database based on a business requirement such as whether an item is likely to arrive at a particular distribution center in sufficient quantity for an expected holiday season based on forecasted supply and/or demand. However, the analyst may not know how to best execute his/her queries without a detailed understanding of the database schema, design, and/or table structure. In addition, the database may be massive, and queries to the database may take many minutes to execute. For this reason, it may be cumbersome for the analyst to execute the query. As a result, the productivity of engineers querying the database may be compromised.